'''simple docstring'''

import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


UpperCamelCase__  :						Any					=      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
  from run_translation import main  # noqa


set_seed(42)
UpperCamelCase__  :						str					=      "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__  :						Union[str, Any]					=      "sshleifer/tiny-mbart"


@require_torch
class 					_lowercase						(   snake_case__ ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=False       ,lowerCamelCase_=None       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.run_trainer(
             eval_steps=1       ,max_len=12       ,model_name=__snake_case       ,num_train_epochs=1       ,distributed=__snake_case       ,extra_args_str=__snake_case       ,predict_with_generate=__snake_case       ,do_train=__snake_case       ,do_eval=__snake_case       ,do_predict=__snake_case       ,)
         UpperCAmelCase__				:					str 					=	TrainerState.load_from_json(os.path.join(__snake_case       ,'''trainer_state.json'''				)				).log_history

         if not do_eval:
            return

         UpperCAmelCase__				:					Optional[Any] 					=	[log for log in logs if '''eval_loss''' in log.keys()]

         UpperCAmelCase__				:					List[Any] 					=	eval_metrics[0]
         if predict_with_generate:
            assert "eval_bleu" in first_step_stats

            UpperCAmelCase__				:					Union[str, Any] 					=	eval_metrics[-1]
            assert isinstance(last_step_stats['''eval_bleu''']       ,__snake_case				)
            assert not math.isnan(float(last_step_stats['''eval_loss''']				)				), "eval_loss must not be `nan`"




      @require_torch_non_multi_gpu
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         self.run_seqaseq_quick()




      @require_torch_multi_gpu
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case				)




      @require_torch_multi_gpu
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case				)




      @unittest.skip('''Requires an update of the env running those tests'''				)
      @require_torch_multi_gpu
      @require_fairscale
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case       ,extra_args_str='''--sharded_ddp simple'''				)




      @unittest.skip('''Requires an update of the env running those tests'''				)
      @require_torch_multi_gpu
      @require_fairscale
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case       ,extra_args_str='''--sharded_ddp simple --fp16'''				)




      @unittest.skip('''Requires an update of the env running those tests'''				)
      @require_torch_multi_gpu
      @require_fairscale
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case       ,extra_args_str='''--sharded_ddp zero_dp_2'''       ,predict_with_generate=__snake_case				)




      @unittest.skip('''Requires an update of the env running those tests'''				)
      @require_torch_multi_gpu
      @require_fairscale
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         self.run_seqaseq_quick(
             distributed=__snake_case       ,extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''       ,predict_with_generate=__snake_case				)




      @require_apex
      @require_torch_gpu
      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         self.run_seqaseq_quick(distributed=__snake_case       ,extra_args_str='''--fp16 --fp16_backend=apex'''				)
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=__snake_case       ,extra_args_str='''--fp16 --fp16_backend=apex'''				)




      @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']				)
      @require_torch_multi_gpu
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	{
             # test with the default log_level - should be info and thus log info once
             '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
         }

         UpperCAmelCase__				:					Optional[int] 					=	experiments[experiment_id]
         UpperCAmelCase__				:					Optional[int] 					=	{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
         UpperCAmelCase__				:					Union[str, Any] 					=	'''Running training'''
         with CaptureStderr() as cl:
            self.run_seqaseq_quick(**__snake_case       ,extra_args_str=data['''extra_args_str''']				)
         UpperCAmelCase__				:					Union[str, Any] 					=	len(re.findall(__snake_case       ,cl.err				)				)
         self.assertEqual(__snake_case       ,data['''n_matches''']				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.run_trainer(
             eval_steps=2       ,max_len=128       ,model_name=__snake_case       ,learning_rate=3e-4       ,num_train_epochs=10       ,distributed=__snake_case       ,)

         # Check metrics
         UpperCAmelCase__				:					Any 					=	TrainerState.load_from_json(os.path.join(__snake_case       ,'''trainer_state.json'''				)				).log_history
         UpperCAmelCase__				:					int 					=	[log for log in logs if '''eval_loss''' in log.keys()]
         UpperCAmelCase__				:					str 					=	eval_metrics[0]
         UpperCAmelCase__				:					Dict 					=	eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['''eval_bleu''']       ,__snake_case				)

         # test if do_predict saves generations and metrics
         UpperCAmelCase__				:					Optional[int] 					=	os.listdir(__snake_case				)
         UpperCAmelCase__				:					List[str] 					=	{os.path.basename(__snake_case				) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents




      @slow
      @require_bitsandbytes
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(lowerCamelCase_				) -> Tuple[int, float]:
            UpperCAmelCase__				:					Union[str, Any] 					=	'''--skip_memory_metrics 0'''

            UpperCAmelCase__				:					Optional[Any] 					=	self.run_trainer(
                max_len=128       ,model_name=__snake_case       ,learning_rate=3e-4       ,num_train_epochs=1       ,optim=__snake_case       ,distributed=__snake_case       ,extra_args_str=__snake_case       ,do_eval=__snake_case       ,do_predict=__snake_case       ,n_gpus_to_use=1       ,)

            # Check metrics
            UpperCAmelCase__				:					Optional[Any] 					=	TrainerState.load_from_json(Path(__snake_case       ,'''trainer_state.json'''				)				).log_history
            UpperCAmelCase__				:					Dict 					=	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20				)
            UpperCAmelCase__				:					List[str] 					=	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20				)

            UpperCAmelCase__				:					List[Any] 					=	logs[0]['''train_loss''']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Any 					=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value				)
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

         UpperCAmelCase__				:					Tuple 					=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase__				:					int 					=	gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase__				:					int 					=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase__				:					Any 					=	gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         UpperCAmelCase__				:					Optional[Any] 					=	120

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             __snake_case       ,__snake_case       ,'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
             f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
             f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''       ,)

         self.assertGreater(
             __snake_case       ,__snake_case       ,'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
             f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
             f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''       ,)

         self.assertEqual(
             __snake_case       ,__snake_case       ,f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_ = 3e-3       ,lowerCamelCase_ = "adafactor"       ,lowerCamelCase_ = False       ,lowerCamelCase_ = None       ,lowerCamelCase_ = 0       ,lowerCamelCase_ = True       ,lowerCamelCase_ = True       ,lowerCamelCase_ = True       ,lowerCamelCase_ = True       ,lowerCamelCase_ = None       ,)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_auto_remove_tmp_dir()
         UpperCAmelCase__				:					Union[str, Any] 					=	f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__snake_case				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__snake_case				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         UpperCAmelCase__				:					Optional[Any] 					=	f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__snake_case				)}
        '''.split()

         UpperCAmelCase__				:					Optional[Any] 					=	'''
            --do_predict
        '''.split()

         UpperCAmelCase__				:					List[Any] 					=	[]
         if do_train:
            args += args_train

         if do_eval:
            args += args_eval

         if do_predict:
            args += args_predict

         if predict_with_generate:
            args += "--predict_with_generate".split()

         if do_train:
            if optim == "adafactor":
               args += "--adafactor".split()
            else:
               args += f'''--optim {optim}'''.split()

         if extra_args_str is not None:
            args += extra_args_str.split()

         if distributed:
            if n_gpus_to_use is None:
               UpperCAmelCase__				:					Union[str, Any] 					=	get_gpu_count()
            UpperCAmelCase__				:					Dict 					=	get_torch_dist_unique_port()
            UpperCAmelCase__				:					str 					=	f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
            UpperCAmelCase__				:					Dict 					=	[sys.executable] + distributed_args + args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(__snake_case       ,env=self.get_env()				)
         else:
            UpperCAmelCase__				:					Tuple 					=	['''run_translation.py'''] + args
            with patch.object(__snake_case       ,'''argv'''       ,__snake_case				):
               main()

         return output_dir








from ..utils import DummyObject, requires_backends

class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




